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Abstract

Gesture-based control for mobile manipulators faces persistent challenges in reliability, efficiency, and intuitiveness. This paper presents a dual-hand gesture interface that integrates TinyML, spectral analysis, and sensor fusion within a ROS framework to address these limitations. The system uses left-hand tilt and finger flexion, captured using accelerometer and flex sensors, for mobile base navigation, while right-hand IMU signals are processed through spectral analysis and classified by a lightweight neural network. This pipeline enables TinyML-based gesture recognition to control a 7-DOF Kinova Gen3 manipulator. By supporting simultaneous navigation and manipulation, the framework improves efficiency and coordination compared to sequential methods. Key contributions include a bimanual control architecture, real-time low-power gesture recognition, robust multimodal sensor fusion, and a scalable ROS-based implementation. The proposed approach advances Human-Robot Interaction (HRI) for industrial automation, assistive robotics, and hazardous environments, offering a cost-effective, open-source solution with strong potential for real-world deployment and further optimization.